Research - EEG analysis and epileptic seizure prediction
This line of research deals with the application of time series analysis to electrophysiological data, in particular to the electroencephalogram (EEG) of epilepsy patients. One important aim is the extraction of information that might be useful for diagnostic purposes. Examples include the localization of the epileptic focus as well as the prediction of epileptic seizures and its statistical evaluation.
Typically in a study on the predictability of epileptic seizures first a characterizing measure is calculated from multi-channel EEG using a moving-window technique. The resulting measure profiles are then scanned for prominent features which can be related to the actual seizure (ictus) times. These features might be drops or peaks (e.g., quantified as threshold crossings) or any other distinct pattern in the measure profile. In a second step the measures' capability to distinguish the preictal from the interictal interval is evaluated with a test statistics quantifying the occurrence of these features relative to the seizure times and resulting in some kind of performance value. The figure below shows an example of a measure profile for a patient's quasi-continuous recording over more than five days including ten seizures (top). The difference in the distributions for the interictal and the preictal intervals (bottom, left) is then quantified by means of a ROC curve (bottom, right). For details see Refs.  and  below.
 Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F:
Monitoring spike train synchrony.
J Neurophysiol 109, 1457 (2013) [PDF].
Also submitted to the arXiv [PDF].
Abstract: Recently, the SPIKE-distance has been proposed as a
parameter-free and time-scale independent measure of spike train synchrony.
This measure is time-resolved since it relies on instantaneous
estimates of spike train dissimilarity. However, its original definition
led to spuriously high instantaneous values for event-like
firing patterns. Here we present a substantial
improvement of this measure which eliminates this
shortcoming. The reliability gained allows us to track changes in
instantaneous clustering, i.e., time-localized
patterns of (dis)similarity among multiple spike trains. Additional new
features include selective and triggered temporal averaging
as well as the instantaneous comparison of spike train groups. In a
second step, a causal SPIKE-distance is defined
such that the instantaneous values of
dissimilarity rely on past information only so that time-resolved spike
train synchrony can be estimated in real-time. We
demonstrate that these methods are capable of extracting valuable
information from field data by monitoring the
synchrony between neuronal spike trains during an epileptic seizure.
Finally, the applicability of both the regular and the real-time
SPIKE-distance to continuous data is illustrated on model
electroencephalographic (EEG) recordings.
 Andrzejak RG, Mormann F, Widman G, Kreuz T, Elger CE, Lehnertz K:
Improved spatial characterization of the epileptic brain by focusing on nonlinearity.
Epilepsy Research 69, 30 (2006).
 Mormann F, Kreuz T, Rieke C, Andrzejak RG, Kraskov A, David P, Elger CE, Lehnertz K:
On the predictability of epileptic seizures.
Clin Neurophysiol 116, 569 (2005).
 Kreuz T, Andrzejak RG, Mormann F, Kraskov A, Stoegbauer H, Elger CE, Lehnertz K, Grassberger P:
Measure profile surrogates: A method to validate the performance of epileptic seizure prediction algorithms.
Phys Rev E 69, 061915 (2004) [PDF].
 Kreuz T:
Measuring synchronization in model systems and electroencephalographic time series from epilepsy patients.
Interdisciplinary PhD thesis in physics, University of Wuppertal, Research Center Juelich (2003).
Supervisors: Prof. P. Grassberger, Research Center Juelich, Germany; Dr. K. Lehnertz, University of Bonn, Germany [PDF].
In the first part of this dissertation the symmetric measures are tested in a controlled setting by means of various model systems. Using the coupling strength as a first control parameter it is investigated to which extent the different measures are able to distinguish between different degrees of dependence. Furthermore, the robustness of the measures against external noise is estimated by varying the signal-to-noise ratio as the second control parameter.
Subsequently, all measures are employed to analyze electroencephalographic recordings from epilepsy patients. This application part consists of two single studies. First a comprehensive comparison on the predictability of epileptic seizures is carried out. Object of investigation is the capability of the different measures to reliably distinguish between the intervals preceding epileptic seizures and the intervals far away from any seizure activity. Already in this study a great deal of attention is paid to the statistical validation of seizure predictions. This issue is particularly addressed in the last part of this dissertation in which the method of measure profile surrogates is introduced as an appropriate tool to distinguish between measures and algorithms unsuited for the prediction of epileptic seizures, and more promising approaches. Two of the measures of synchronization are used to illustrate this new approach.
 Rieke C, Mormann F, Andrzejak RG, Kreuz T, David P, Elger CE, Lehnertz K:
Discerning nonstationarity from nonlinearity in seizure-free and pre-seizure EEG recordings from epilepsy patients.
IEEE Trans Biomed Eng 50, 634 (2003).
 Mormann F, Kreuz T, Andrzejak RG, David P, Lehnertz K, Elger CE:
Epileptic seizures are preceded by a decrease in synchronization.
Epilepsy Res 53, 171 (2003).
 Mormann F, Andrzejak RG, Kreuz T, Rieke C, David P, Elger CE, Lehnertz K:
Automated detection of a pre-seizure state based on a decrease in synchronization in intracranial EEG recordings from epilepsy patients.
Phys Rev E 67, 021912 (2003).
 Lehnertz K, Mormann F, Kreuz T, Andrzejak RG, Rieke C, David P, Elger CE:
Seizure prediction by nonlinear EEG analysis.
IEEE Trans Biomed Eng (Special Issue: Epileptic Seizure Prediction: Models and Devices) 22, 57 (2003).
 Andrzejak RG, Mormann F, Kreuz T, Rieke C, Kraskov A, Elger CE, Lehnertz K:
Testing the null hypothesis of the non-existence of a pre-seizure state.
Phys Rev E 67, 010901 (2003).
 Lehnertz K, Andrzejak RG, Arnhold J, Kreuz T, Mormann F, Rieke C, Widman G, Elger CE:
Nonlinear EEG analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention.
J Clin Neurophysiol 18, 209-222 (2001).